Reached maximum number of idle transformation calls.

[hshenai]

I have multiple time-series with multiple targets that needs to be predicted. When I try to train I get the below error. Please Help

# Convert DataFrame to GluonTS-compatible format
time_series_data = []
unique_time_series = df['store_id'].unique()

target_columns = ['S1', 'S2', 'S3']

for time_series_id in unique_time_series:
    subset = df[df['storeId'] == time_series_id].sort_values('time').reset_index(drop=True)
    time_series_data.append({
        'target': subset[target_columns].values,
        'start': subset['time'].iloc[0],
        'feat_dynamic_cat': subset['shop_type'].values.reshape((-1, 1))
    })
    
# Define the frequency of the time series 
freq = '5T'

dataset = ListDataset(time_series_data, freq=freq, one_dim_target=False)

# Print the first entry in the dataset
print(next(iter(dataset)))

dataset[1]['feat_dynamic_cat'].shape
(8872, 1)
dataset[1]['target'].shape
(8872, 3)

prediction_length = 12
context_length = 12
num_cells = 32
num_layers = 2
epochs= 2
freq="5T"

estimator = DeepVAREstimator(freq=freq, 
                            context_length=context_length,
                            prediction_length=prediction_length,
                            num_layers=num_layers,
                            num_cells=num_cells,
                            target_dim=3,
                            cardinality=[1], # Need to fill the len of unique switch types
                            trainer=Trainer(epochs=epochs))
predictor = estimator.train(dataset)

 0%|                                                                                                                                            | 0/50 [00:00<?, ?it/s]
---------------------------------------------------------------------------
Exception                                 Traceback (most recent call last)
/var/folders/mc/ph4ybm0x2mld5hj1nch6fpdr0000gn/T/ipykernel_16981/1101355604.py in <module>
----> 1 predictor = estimator.train(dataset)

~/opt/anaconda3/lib/python3.9/site-packages/gluonts/mx/model/estimator.py in train(self, training_data, validation_data, shuffle_buffer_length, cache_data, **kwargs)
    235         **kwargs,
    236     ) -> Predictor:
--> 237         return self.train_model(
    238             training_data=training_data,
    239             validation_data=validation_data,

~/opt/anaconda3/lib/python3.9/site-packages/gluonts/mx/model/estimator.py in train_model(self, training_data, validation_data, from_predictor, shuffle_buffer_length, cache_data)
    212             copy_parameters(from_predictor.network, training_network)
    213 
--> 214         self.trainer(
    215             net=training_network,
    216             train_iter=training_data_loader,

~/opt/anaconda3/lib/python3.9/site-packages/gluonts/mx/trainer/_base.py in __call__(self, net, train_iter, validation_iter)
    418                     )
    419 
--> 420                     epoch_loss = loop(
    421                         epoch_no,
    422                         train_iter,

~/opt/anaconda3/lib/python3.9/site-packages/gluonts/mx/trainer/_base.py in loop(epoch_no, batch_iter, num_batches_to_use, is_training)
    273                 any_batches = False
    274 
--> 275                 for batch_no, batch in enumerate(it, start=1):
    276                     any_batches = True
    277 

~/opt/anaconda3/lib/python3.9/site-packages/tqdm/std.py in __iter__(self)
   1178 
   1179         try:
-> 1180             for obj in iterable:
   1181                 yield obj
   1182                 # Update and possibly print the progressbar.

~/opt/anaconda3/lib/python3.9/site-packages/gluonts/itertools.py in __iter__(self)
    179 
    180     def __iter__(self):
--> 181         yield from itertools.islice(self.iterable, self.length)
    182 
    183 

~/opt/anaconda3/lib/python3.9/site-packages/gluonts/transform/_base.py in __iter__(self)
    101 
    102     def __iter__(self) -> Iterator[DataEntry]:
--> 103         yield from self.transformation(
    104             self.base_dataset, is_train=self.is_train
    105         )

~/opt/anaconda3/lib/python3.9/site-packages/gluonts/transform/_base.py in __call__(self, data_it, is_train)
    122         self, data_it: Iterable[DataEntry], is_train: bool
    123     ) -> Iterator:
--> 124         for data_entry in data_it:
    125             try:
    126                 yield self.map_transform(data_entry.copy(), is_train)

~/opt/anaconda3/lib/python3.9/site-packages/gluonts/dataset/loader.py in __call__(self, data, is_train)
     35 
     36     def __call__(self, data, is_train):
---> 37         yield from batcher(data, self.batch_size)
     38 
     39 

~/opt/anaconda3/lib/python3.9/site-packages/gluonts/itertools.py in get_batch()
     98 
     99     def get_batch():
--> 100         return list(itertools.islice(it, batch_size))
    101 
    102     # has an empty list so that we have a 2D array for sure

~/opt/anaconda3/lib/python3.9/site-packages/gluonts/transform/_base.py in __call__(self, data_it, is_train)
    122         self, data_it: Iterable[DataEntry], is_train: bool
    123     ) -> Iterator:
--> 124         for data_entry in data_it:
    125             try:
    126                 yield self.map_transform(data_entry.copy(), is_train)

~/opt/anaconda3/lib/python3.9/site-packages/gluonts/transform/_base.py in __call__(self, data_it, is_train)
    122         self, data_it: Iterable[DataEntry], is_train: bool
    123     ) -> Iterator:
--> 124         for data_entry in data_it:
    125             try:
    126                 yield self.map_transform(data_entry.copy(), is_train)

~/opt/anaconda3/lib/python3.9/site-packages/gluonts/transform/_base.py in __call__(self, data_it, is_train)
    187                 and num_idle_transforms > self.max_idle_transforms
    188             ):
--> 189                 raise Exception(
    190                     "Reached maximum number of idle transformation"
    191                     " calls.\nThis means the transformation looped over"

Exception: Reached maximum number of idle transformation calls.
This means the transformation looped over 113 inputs without returning any output.
This occurred in the following transformation:
gluonts.transform.split.InstanceSplitter(dummy_value=0.0, forecast_start_field='forecast_start', future_length=6, instance_sampler=ExpectedNumInstanceSampler(axis=-1, min_past=54, min_future=6, num_instances=1.0, total_length=0, n=0), is_pad_field='is_pad', lead_time=0, output_NTC=True, past_length=54, start_field='start', target_field='target', time_series_fields=['time_feat', 'observed_values'])

[hshenai]

My Bad, the issue was with the target dimension. The below code changes fixed the issue

for time_series_id in unique_time_series:
    subset = df[df['switchEntity'] == time_series_id].sort_values('windowStart').reset_index(drop=True)
    time_series_data.append({
        'target': subset[target_columns].values.T,
        'start': subset['windowStart'].iloc[0],
        'feat_dynamic_cat': subset['device_type'].values.reshape((-1, 1))
    })